Dynamic modeling of geospatial words in social media

ABSTRACT

Dynamically modelling geospatial words in social media, in one aspect, generates a word set based on frequencies of words occurring in GPS annotated text data generated by a GPS-enabled device containing latitude and longitude coordinates. Locations are partitioned by mapping GPS coordinates in the GPS annotated text data to a set of discrete non-overlapped locations. A text stream contained in the GPS annotated text data is segmented into time windows. Footprints of locations in time windows are generated. Geospatial weights associated with words in the word set are generated based on localness of words determined based on the footprints. Words in a text message are extracted and scores are determined for the set of discrete non-overlapped locations associated with the words.

FIELD

The present application relates generally to computers and computerapplications, and more particularly to dynamic modelling of geospatialwords in social media.

BACKGROUND

Geospatial information is useful to a number of applications, such astargeted advertising, regional sentiment analysis and situationalawareness. Due to a lack of sufficient and reliable geographicalinformation in social media (e.g., Internet Protocol (IP) addressesmapped to locations), various geotagging methods have been utilized toinfer geographical location based on text data. Such geotagging methodsleverage location indicative words to determine location. For instance,by knowing local sports event footy and a local transportation tram, themost probable location inferred is City X, Country Y, because thesewords together are mostly used by City X residents.

Streaming text data in social media is dynamic, i.e., its content andtopics change rapidly, making geotagging a non-trivial task. Existinggeotagging models are often trained in an off-line manner, and thisimplies these models do not capture the temporal variance of geospatialwords, when time-invariant geospatial words are persistently associatedwith a location, while other words are only temporarily associated witha location.

BRIEF SUMMARY

A dynamic geospatial word modelling that keeps geotagging modelsup-to-date is presented. A method of dynamically modeling geospatialwords, for example, in social media data, in one aspect, may comprisereceiving GPS annotated text data generated by a GPS-enabled devicecontaining latitude and longitude coordinates. The method may alsoinclude generating a word set based on frequencies of words occurring inthe GPS annotated text data. The method may further include partitioninglocations by mapping GPS coordinates in the GPS annotated text data to aset of discrete non-overlapped locations. The method may also includesegmenting a text stream contained in the GPS annotated text data intotime windows. The method may further include generating footprints oflocations in time windows. The method may further include determininggeospatial weights associated with words in the word set based onlocalness of words determined based on the footprints. The method mayalso include dynamically integrating in geotagging by extracting wordsin a text message and determining scores associated with the set ofdiscrete non-overlapped locations.

A system for dynamically modeling geospatial words, for example insocial media, in one aspect, may comprise a data collector operable toexecute on a processor and further operable to receive GPS annotatedtext data generated by a GPS-enabled device containing latitude andlongitude coordinates. A model trainer may be operable to execute on theprocessor and further operable to generate a word set based onfrequencies of words occurring in the GPS annotated text data. The modeltrainer may be further operable to partition locations by mapping GPScoordinates in the GPS annotated text data to a set of discretenon-overlapped locations. The model trainer may be further operable tosegment a text stream contained in the GPS annotated text data into timewindows. The model trainer may be further operable to generatefootprints of locations in time windows. The model trainer may befurther operable to determine geospatial weights associated with wordsin the word set based on localness of words determined based on thefootprints. The model trainer may be further operable to dynamicallyintegrate geotagging by extracting words in a text message anddetermining scores associated with the set of discrete non-overlappedlocations. A storage device coupled to the processor may be operable tostore the footprints and GPS labeled data, the GPS labeled datagenerated based on mapping the words in the word set to a respectivelocation in the set of discrete non-overlapped locations.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram illustrating a method of dynamic modelling ofgeo spatial words in social media in one embodiment of the presentdisclosure.

FIG. 2 shows a bipartite graph constructed between word types inmessages and mapped locations in a plurality of time windows.

FIG. 3 is a diagram illustrating system components that dynamicallymodel geospatial words in data in one embodiment of the presentdisclosure

FIG. 4 illustrates a schematic of an example computer or processingsystem that may implement a dynamic modelling system in one embodimentof the present disclosure.

DETAILED DESCRIPTION

A method and system are presented that leverage location indicativewords to determine location. The method and system for example mayoffset the temporal variance in geospatial word modelling by dynamicallyweighting word localness scores in social media text stream. A dynamicgeospatial word modelling technique in one embodiment keeps geotaggingmodels up-to-date. In one aspect, the techniques keep the modelsup-to-date with only fixed computational cost. In one embodiment,randomized methods may be applied to generate consecutive locationfootprints for each word over time. Word localness scores are calculatedbased on these footprints. In one embodiment, the overall word localnesscan be incrementally updated with only fixed computational cost. Adiscriminative model in one embodiment then seamlessly incorporatesupdated localness scores in geotagging. As a result, the geotaggingmodel is kept up-to-date without re-training the model on all historicaldata. Such an approach, for example, is useful in stream computingenvironment in which endless data is transmitted and received.

Hence, instead of having to retrain a model over all historical data,the randomized methods in one embodiment of the present disclosure keepgeotagging model up-to-date with only fixed computational cost. Inanother aspect, a system that implements the methods may be adaptive tothe computational capability, e.g., by tuning parameters in the system,geotagging models can be built with limited resource.

FIG. 1 is a flow diagram illustrating a method of dynamic modelling ofgeo spatial words in social media in one embodiment of the presentdisclosure.

To associate words with locations a set of ground truth data, forexample, global positioning system (GPS) annotated text, may be used.Such data can be obtained or collected via a number of ways, forinstance, social media messages sent from GPS-enabled mobile devicesthat contain latitude and longitude coordinates. At 102, GPS annotatedtext data is obtained or received. For example, social media messagegenerated by a GPS-enabled device containing latitude and longitudecoordinates may be obtained. In one aspect, GPS annotated text data mayinclude a pre-existing collection of GPS-labeled text corpora. This datamay be used at 104 to generate all words (i.e., the word set) whoselocalness the system and method of the present disclosure in oneembodiment tracks over time, forming an initial geotagging model. In oneembodiment, the GPS labels of the GPS annotated text data are also usedin location partition at 106.

At 104, word set generation is performed, for example, based on theobtained GPS annotated text data. The word set generation process in oneembodiment generates a word type set {w_(i)|iε1, . . . , n} from acollection of social media data, the obtained GPS annotated data. “n”represents the number of words in a set. Words are tokenized and countedand then a user-defined threshold (e.g., 100) is applied to preservesufficiently frequent words. For example, words occurring in thecollection of data in frequency that meets a frequency threshold areincluded in the word set.

At 106, location partition is performed. All GPS coordinates in the GPSannotated text data are mapped to a set of discrete non-overlappedlocations, e.g., metropolitan city centers {c_(j)|jε1, . . . , m}. “m”represents the number of discrete non-overlapped locations in a set ofdiscrete non-overlapped locations.

At 108, a live text stream is segmented into time windows. A live textstream refers to new GPS text data that are continuously harvested fromsocial media feed, for example, now and in the future. In oneembodiment, the live stream data is used to evaluate the word localnessover time. In one embodiment, a constant text stream is partitioned intoincreasing consecutive time windows {T_(p)|pε1, . . . , t}. “t”represents the number of time periods or time windows. A time windowcontains data generated in a time period, e.g., hourly, daily or weekly.In one embodiment, a time window length is user specified orconfigurable, depending on the computational resources and incoming datarates.

At 110, footprint is generated in time windows. For each GPS-labeledmessage in a time window (e.g., Tp), a bipartite graph is constructedbetween a word type (e.g., wi) in the message and the mapped location(e.g., cj). An example of a GPS-labeled message may include a socialmedia message that includes location or GPS information, e.g., amicroblog with GPS information such as the geospatial coordinates ofwhere the microblog was generated, a social network content posting withembedded with GPS data, or another message with embedded data structurewith location or GPS information. GPS coordinates, for example, can bemapped to a point on earth. GPS information or coordinates may be mappedto non-overlapped discrete locations. A message with GPS informationaccordingly may be transformed into a message associated with anon-overlapped discrete location. In one embodiment, the nearestlocation in the pre-partitioned location set to this point can bedetermined to be the mapped location. For instance, the mapped locationfor (−37.813,144.963) is Melbourne, Australia. The association strengthis the location distribution of wi, i.e., co-occurrences of wi and alocation normalized by all occurrences of wi in Tp. For example,consider that w1 occurs n_j times in cj, respectively, during Tp. Theassociation strength between w1 and c1 may be determined asn_1/(n_1+n_2+ . . . +n_m). The denominator is the normalization factorwhich is the sum of all occurrences of w1 during Tp. The methodologythen selects k (<m) locations relative to the association strength forwi in Tp, as shown in FIG. 2. k represents draws from locations where wioccurs in Tp. Some locations may be selected multiple times due to thedifferent association strength. For instance, if 10 draws were obtainedfrom 100 occurrences of word w1 in two locations c1, c2, and if thestrength numbers are 0.9 and 0.1 respectively, then the expectedselections of 10 draws would be 9 c1 and 1 c2. These k locations are thegenerated footprint, and the footprint is parameterized by word type wi,time window Tp, and the user-specified number of random selections k.

FIG. 2 shows a bipartite graph constructed between word types inmessages and mapped locations in a plurality of time windows. FIG. 2shows a reconstructed graph based on a series of bipartite graphs overtime. The columns Words—T1, Words—T2 and Words—T3 may be viewed asbipartite graphs between words and locations in consecutive timeperiods, T1, T2, T3, . . . Tt. Each bipartite graph shows the locationsassociated with a word type. For instance, the bipartite graph exampleof Words—T1 shows that w1 is associated with locations, c1, c2, and c3,when k=10. For instance, the numbers shown in edges between w1 andlocations c1, c2 and c3 in T1 suggest that c1, c2 and c3 are selected 6,3, 1 times, respectively. Similarly, for w1 in time window T2, c1, c2and c3 are selected 8, 1, 1 times, respectively. Likewise, for w1 intime window T3, c1, c2, c3 and c4 are selected 7, 1, 1, 1 times,respectively. The processing at 110 may sample the number of locations aword is associated with, e.g., to reduce the size and thereforecomputational cost of the model.

At 112, word localness calculation is performed for a footprint, forexample, generated at 110. For each word wi, a methodology of thepresent disclosure in one embodiment calculates localness score in eachtime window Tp. The localness score can be implemented in various ways,provided the location indicativeness can be differentiated. Forinstance, the reciprocal of the footprint entropy (with additivesmoothing) can be applied to calculate the localness score of wi. In oneembodiment, a localness score of wi may be computed as follows:

${{localness}( {i,p,k} )} = \frac{1}{1 - {\sum\limits_{1}^{m}{{prob}_{j}\log\;{prob}_{j}}}}$

As the above formula shows, for a word wi, pj is the re-normalizedassociation strength between wi and cj in the previous k selections. Forinstance, in Words—T1 bipartite graph shown in FIG. 2, localness scorefor w1 is 1/(1+(−6/10 log 6/10)+(−3/10 log 3/10)+(−1/10 log 1/10)).Entropy herein refers to information entropy, which is defined on adiscrete probability distribution, Entropy(P)=−Σ₁ ^(m) prob_(j) logprob_(j), in which prob is a probability for a possible event j indistribution P. A high localness score indicates a skewed distributionof footprint (i.e., a low entropy), and therefore wi is a local word. Ahigh localness score means a low entropy as defined in previouslocalness formula and the information entropy definition, and a lowentropy indicates a skewed distribution as a property of informationentropy. This entropy description is an analog of word frequency indifferent locations. A low localness score suggests wi occur in manylocations (i.e., a high entropy), and it is unlikely to be locationindicative. The localness score is set to zero when a word has fewerthan k locations in a time window. In one embodiment, time windows areconstantly obtained over time, i.e., Tp in p|pε1, . . . , t isincreasing. The localness scores of each time window are then added upas the word geospatial weight in one embodiment as follows:weight[w _(i)]=Σ_(p=1) ^(t) localness(i,p,k)

The notations for i and k refer to a word type (e.g., w_(i) for i from 1to n) and k location selections described above. Localness(i, p, k)represents the localness score of word wi during time period Tp whenselecting k locations in the calculation.

A time-invariant location indicative word would have consecutive highlocalness scores leading to a high geospatial weight. In contrast, thelocalness scores fluctuate for temporal local words, i.e., some timewindows have high localness scores, while others have low localnessscores. The temporal variance of word location indicativeness istherefore captured in the accumulated geospatial weight. The accumulatedgeospatial weight incurs a fixed computational cost, because onlylocalness score of new time windows are required to be calculated andadded-up.

At 114, dynamic integration of word localness in geotagging isperformed. For example, geotagging may be performed for text messages,which may not include GPS coordinates. When geotagging a text message,words are extracted and are used as evidence to score locations. Forexample, a new message or text message (that does not include locationindication) may be received and geographical location associated thisnew message may be determined or predicted. In one embodiment, thegeotagging prediction is based on the following formula:

${\arg\;\max\; c_{j}} = {\exp( {\sum\limits_{i = 1}^{n}{{P( c_{j} \middle| w_{i} )}{{weight}\lbrack w_{i} \rbrack}}} )}$

The above formula finds a location among all potential locations withthe highest value as computed in the right hand side of the equation.P(cj|wi), the probability of cj given wi, can be obtained by theaccumulated location distribution over time for word wi. For instance,referring to FIG. 2, suppose current period is T3. Using w1 as example,w1 occurs (6+8+7) times in c1, (3+1+1) times in c2, (1+1+1) times in c3and 1 in c4. As a result, p(c1|w1) would be(6+8+7)/((6+8+7)+(3+1+1)+(1+1+1)+1). In one embodiment, only cj withnon-zero P(cj|wi) are eligible to be selected as potential predictions.weight[wi] is the geospatial weight for wi. Its dynamic values ensureup-to-date geotagging predictions. The cj with the highest right handvalue is determined to be the predicted location in one embodiment.Geotagging takes a text message as the input and outputs a predictedlocation using words in the text message. In one embodiment, thesecomponents (weight[wi] and p(ci|wi)), which are updated periodically,are additive and are only updated when a new time window is obtained.

At 116, time window sampling is performed. Sampling may employ a randomselection process. The sampling of time windows in one embodimentsamples a fixed number of time windows, e.g., to maintain a constantcomputational cost for the model as the GPS labeled dataset increaseswith time.

The number of time windows t increases over time. To adapt the systemcomputational capability to different hardware, time window sampling isalso introduced in one embodiment. For instance, instead of usinginformation from all time windows, only sampled time windows are used.The sampling strategy can be implemented in various ways depending onapplication scenarios. For instance, reservoir sampling ensures amethodology of the present disclosure get a fixed size unbiased sampleof time windows. In contrast, a fixed first-in-first-out queue capturesthe most recent time windows to cope with rapidly changing text streamdata. Any one or more of the above sampling or another sampling may beadopted in the present disclosure.

FIG. 3 is a diagram illustrating system components that dynamicallymodel geospatial words in data in one embodiment of the presentdisclosure. A storage device 302 or mechanism stores the GPS-labeleddata generated since the last time the model has been refined. A storagedevice 304 or mechanism stores the footprints generated and chosen, forexample, as described above with reference to FIG. 1 at 110 and 116. Newfootprints are stored at 304 as future refinement and iteration of themodel. A model trainer 306 may be a computer executable that runs on oneor more processors, e.g., with main memory, and trains the model (orgenerates the model by training it with data) for geotagging messages,for example, as described above with reference to FIG. 1 at 102 to 112.A data collector 308 collects data, e.g., text messages that include GPSor location data, e.g., via a communication network. The collected datais used to train the model, e.g., by the model trainer 306. A modeldeployer 310 may run the trained model to predict location informationfor new text message or data. The model trainer 306, data collector 308and model deployer 310 may be computer executable objects that run onone or more processors. A network connection facilitates collection ofthe data and use of the model by external services, over the network,for example, via an application programming interface (API) for themodel.

FIG. 4 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment of the presentdisclosure. The computer system is only one example of a suitableprocessing system and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the methodologydescribed herein. The processing system shown may be operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with the processing system shown in FIG. 4 may include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 10 that performsthe methods described herein. The module 10 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

We claim:
 1. A method of dynamically modeling geospatial words in socialmedia, comprising: receiving GPS annotated text data generated by aGPS-enabled device containing latitude and longitude coordinates;generating a word set based on frequencies of words occurring in the GPSannotated text data; partitioning locations by mapping GPS coordinatesin the GPS annotated text data to a set of discrete non-overlappedlocations; segmenting a text stream contained in the GPS annotated textdata into time windows; generating footprints of locations in timewindows; determining geospatial weights associated with words in theword set based on localness of words determined based on the footprints;dynamically integrating in geotagging by extracting words in a textmessage and determining scores associated with the set of discretenon-overlapped locations.
 2. The method of claim 1, further comprising:sampling a fixed number of the time windows in the segmenting and thegenerating steps.
 3. The method of claim 1, wherein the generatingfootprints of locations in time windows comprises, for each GPSannotated text data in a time window, constructing a bipartite graphbetween a word type of the GPS annotated text data and a mappedlocation.
 4. The method of claim 3, wherein the generating footprints oflocations further comprises determining an association strength betweenthe word type and the mapped location.
 5. The method of claim 4, furthercomprising selecting a predetermined number of locations based on theassociation strength as the footprints, the footprints parameterized byassociated word type, time window, and the predetermined number.
 6. Themethod of claim 1, further comprising predicting location informationfor a new text message based on words in the new text message and thegeospatial weights.